Impacts of Tai Chi on Empathy Levels in Healthcare Professionals: A Quasi-Experimental Study
Bibliographic record
Abstract
Background: Healthcare professionals may struggle with empathy, which can affect patient care. It is possible that Tai Chi, which includes physical activity and mindfulness, could potentially impact empathy levels. The purpose of this study was to investigate the impact of Tai Chi on empathy levels in healthcare professionals, using the Toronto Empathy Questionnaire. Materials and Methods: Five females 18-60 years of age who were employed as healthcare workers in the United States for at least six months were asked to complete asynchronous virtual Tai Chi sessions for 30 minutes, two times a week, for 5 weeks. Each Tai Chi virtual session included safety reminders, a warm-up, a cool-down, and different Tai Chi moves integrated into a sequence of movements. The Toronto Empathy Questionnaire was utilized to assess the participants’ empathy levels pre-and post-intervention. Participants completed weekly progress logs to track engagement. Results: Based on the weekly progress logs, participants completed an average of 8 asynchronous virtual Tai Chi sessions over 5 weeks. Participants demonstrated an average increase of 4.2 points on the Toronto Empathy Questionnaire pre- to post-intervention (mean pretest TEQ score = 48.2; mean posttest TEQ score = 52.4). The participants’ average scores on the TEQ posttest averaged 3.47 points above the typical female score range. Conclusion: Results suggest the potential value of Tai Chi for enhancing empathy levels in healthcare professionals. Future studies should integrate larger sample sizes, and qualitative approaches to data collection focused on participants' perspectives regarding their displayed empathy levels. Another suggestion is synchronous Tai Chi sessions, which could increase participant accountability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".